Pokémon Trading Card Game Python API Docs | dltHub

Pokemon TCG API is a REST API that provides programmatic access to Pokemon Trading Card Game data (cards, sets, types, rarities, and related metadata). The REST API base URL is https://api.pokemontcg.io/v2 and all requests may be unauthenticated but production use requires an API key provided via the X-Api-Key header for higher rate limits.

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Pokémon Trading Card Game data in under 10 minutes.

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What data can I load from Pokémon Trading Card Game?

Here are some of the endpoints you can load from Pokémon Trading Card Game:

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How do I authenticate with the Pokémon Trading Card Game API?

The API accepts an API key in the X-Api-Key HTTP header. Requests over HTTPS are required; unauthenticated requests are allowed but have much lower rate limits.

1. Get your credentials

1. Visit https://dev.pokemontcg.io and sign up or log in. 2) Create/generate a new API key from the Developer Portal. 3) Copy the key and include it in requests using the X-Api-Key header.

2. Add them to .dlt/secrets.toml

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[sources.pokemon_trading_card_game_source]
api_key = "your_api_key_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Pokémon Trading Card Game API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python pokemon_trading_card_game_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline pokemon_trading_card_game_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset pokemon_trading_card_game_data
The duckdb destination used duckdb:/pokemon_trading_card_game.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline pokemon_trading_card_game_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads cards and sets from the Pokémon Trading Card Game API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def pokemon_trading_card_game_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.pokemontcg.io/v2",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "cards", "endpoint": {"path": "v2/cards", "data_selector": "data"}},
            {"name": "sets", "endpoint": {"path": "v2/sets", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pokemon_trading_card_game_pipeline",
        destination="duckdb",
        dataset_name="pokemon_trading_card_game_data",
    )
    load_info = pipeline.run(pokemon_trading_card_game_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("pokemon_trading_card_game_pipeline").dataset()
sessions_df = data.cards.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM pokemon_trading_card_game_data.cards LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("pokemon_trading_card_game_pipeline").dataset()
data.cards.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Pokémon Trading Card Game data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive 401/403 or rapidly limited behavior, confirm you included X-Api-Key with a valid key, and that requests use HTTPS. Unauthenticated requests are permitted but have very low rate limits.

Rate limits

Rate limits depend on whether you provide an API key. Without a key limits are low; with a key you get higher limits. If you hit limits, the API will return standard HTTP 429 responsesimplement exponential backoff and respect pageSize (max 250) when paginating.

Pagination quirks

Card searches use page and pageSize (default page=1, max pageSize=250). Results are returned in the "data" array; request next page to iterate. Use select to reduce payload size.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack